On the implementation of baselines and lightweight conditional model extrapolation (LIMES) under class-prior shift
P. Tomaszewska, C. Lampert, in:, International Workshop on Reproducible Research in Pattern Recognition, Springer Nature, 2023, pp. 67–73.

Deep neural collapse is provably optimal for the deep unconstrained features model
P. Súkeník, M. Mondelli, C. Lampert, in:, 37th Annual Conference on Neural Information Processing Systems, n.d.

1-Lipschitz neural networks are more expressive with N-activations
B. Prach, C. Lampert, ArXiv (n.d.).

CrAM: A Compression-Aware Minimizer
E.-A. Peste, A. Vladu, E. Kurtic, C. Lampert, D.-A. Alistarh, in:, 11th International Conference on Learning Representations , n.d.

Overcoming rare-language discrimination in multi-lingual sentiment analysis
J. Lampert, C. Lampert, in:, 2021 IEEE International Conference on Big Data, IEEE, 2022, pp. 5185–5192.

Fairness-aware PAC learning from corrupted data
N.H. Konstantinov, C. Lampert, Journal of Machine Learning Research 23 (2022) 1–60.

On the impossibility of fairness-aware learning from corrupted data
N.H. Konstantinov, C. Lampert, in:, Proceedings of Machine Learning Research, ML Research Press, 2022, pp. 59–83.

Almost-orthogonal layers for efficient general-purpose Lipschitz networks
B. Prach, C. Lampert, in:, Computer Vision – ECCV 2022, Springer Nature, 2022, pp. 350–365.

Lightweight conditional model extrapolation for streaming data under class-prior shift
P. Tomaszewska, C. Lampert, in:, 26th International Conference on Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 2128–2134.

FLEA: Provably robust fair multisource learning from unreliable training data
E.B. Iofinova, N.H. Konstantinov, C. Lampert, Transactions on Machine Learning Research (2022).

Cross-client Label Propagation for transductive federated learning
J.A. Scott, M.X. Yeo, C. Lampert, ArXiv (n.d.).

Generalization in Multi-objective machine learning
P. Súkeník, C. Lampert, ArXiv (n.d.).

Fairness through regularization for learning to rank
N.H. Konstantinov, C. Lampert, ArXiv (n.d.).

Zero-Shot Learning
C. Lampert, in:, K. Ikeuchi (Ed.), Computer Vision, 2nd ed., Springer, Cham, 2021, pp. 1395–1397.

Does SGD implicitly optimize for smoothness?
V. Volhejn, C. Lampert, in:, 42nd German Conference on Pattern Recognition, Springer, 2021, pp. 246–259.

The inductive bias of ReLU networks on orthogonally separable data
M. Phuong, C. Lampert, in:, 9th International Conference on Learning Representations, 2021.

Localizing grouped instances for efficient detection in low-resource scenarios
A. Royer, C. Lampert, in:, IEEE Winter Conference on Applications of Computer Vision, IEEE, 2020.

A flexible selection scheme for minimum-effort transfer learning
A. Royer, C. Lampert, in:, 2020 IEEE Winter Conference on Applications of Computer Vision, IEEE, 2020.

Object-centric image generation with factored depths, locations, and appearances
T. Anciukevicius, C. Lampert, P.M. Henderson, ArXiv (n.d.).

Leveraging 2D data to learn textured 3D mesh generation
P.M. Henderson, V. Tsiminaki, C. Lampert, in:, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2020, pp. 7498–7507.